
The claim on the bias introduced by block-design in [1]} cannot be generalized to our case, while it appears that the data in [1]} is highly contaminated by temporal correlation;

The reason for such a huge bias in [1]} is due to a failure in replicating the block design in [4]} (which instead is compliant with EEG/ERP study design recommendations). Drastically increasing the session duration (over 23 minutes compared to the about 4 minutes in [4]}) can only reduce the subjects' vigilance level, increasing the presence of the temporal correlation in the EEG data and its effect on classification performance;

The rapid-design experiment and settings proposed in [1]}, are rather uncommon in cognitive neuroscience and are bound to maximize the temporal correlation;

The temporal correlation bias on classification results is further exacerbated by the per-subject analysis in [1]}, in contrast to [4]};

Thus, [1]} did fully not replicate [4]}'s settings, both from cognitive neuroscience and from machine learning perspective, by a) altering the experiments and session duration and performing EEG analyses per-subject rather than pooled data analysis, and b) incorporating a new experimental design (i.e., the rapid-design) that does not have any scientific foundation and differs significantly, in terms of expected neural responses, from [4]}.

The analysis by [1]} of the works , , ,  is flawed due to wrong and unsubstantiated claims, misinterpretation of results and of basic machine learning concepts.

These above two issues, combined to the rather qualitative analysis of the state of the art, focusing only on a few works sharing the same subset of authors (us), and to the speculative nature of multiple arguments, calls into question the actual intention of [1]}.

